Process for monitoring traffic for automatic vehicle incident detection

ABSTRACT

A process for monitoring traffic for automatic vehicle incident detection using radar waves to detect the vehicles, their instantaneous speed and their distance. The process consists in correlating the information obtained regarding vehicles in one and the same distance bracket during two consecutive processing time intervals, so as to determine, at each instant, the acceleration of each vehicle and a prediction of its speed, and in then detecting an incident in a distance bracket by detecting the passing of the speed of a vehicle below a given speed threshold. The advantages of the process resides in rapid incident detection in regard to a road or motorway network, with a view to informing the users rapidly.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The subject of the present invention is a process for monitoring trafficfor automatic vehicle incident detection.

The application at which the invention is more particularly aimedrelates to the monitoring of road or motorway traffic, conventionallyreferred to generically as Automatic Incident Detection (AID).

In this application it is sought especially to be able to detect avehicle coming to a stop on traffic lanes or on hard shoulders. Otherparameters may beneficially be supplied, such as the detection ofholdups or the state of the traffic flow.

Monitoring the traffic on a road or motorway is very important since itsaim is to improve the fluidity of the traffic and the safety of users.It is therefore paramount to know how to detect any incident or slowdownas quickly as possible so as to forewarn the users via variable-messageroad signs, thereby limiting the risks of pileups, and to involve theemergency services rapidly if necessary.

2. Discussion of the Background

The monitoring of road traffic is presently undertaken using varioustechniques which may be classed into two categories, on the one handtechniques based on pointwise analysis of a section of road or ofmotorway and, on the other hand, techniques based on overall analysis ofthis same section.

In the first case, only certain points of the road, located atpredetermined distances from one another, are observed. Analysis oftraffic parameters at these various points, such as the mean speed ofthe vehicles or the flow rate, makes it possible to detect, by applyingspecial computational algorithms, the consequences of a possibleincident between these points.

A first very widely used technology consists in placing induction loopsunder the road. The variation in the field induced in such loops makesit possible to ascertain whether a vehicle has or has not passed. Themain advantage of this technology lies in the fact that the inductionloops operate irrespective of climatic conditions, by day and by night.However, it is unwieldy and expensive to install these loops, and isdifficult, or even impossible, to carry out maintenance or to replaceloops in the event of a breakdown.

A second technology consists in video cameras located at the variouspoints which one wishes to analyse over a given section, each camerabeing associated with an automatic processing of images. It is verysimple to install the video cameras, but their performance isinconclusive since it depends strongly on the climatic conditions and onthe lighting conditions. Furthermore, the algorithms used in imageprocessing are complex and require considerable computational power.

To alleviate the problem of the climatic conditions, a third knowntechnology, again implementing a technique of pointwise analysis of asection of road or of motorway, employs a radar, either acontinuous-wave stationary or pulsed Doppler radar. A technique whichuses a continuous-wave stationary radar to extract various parameters,such as speed or length, is for example described in the document U.S.Pat. No. 4,985,705. Moreover, refer to the document FR 2,695,742 filedby the Applicant for the description of a pulsed Doppler radar, whoseparticular positioning and associated algorithm make it possible toextract various parameters (especially the number of vehicles pertraffic lane, the speed of the vehicles, the length of the vehicles).

The major drawback of the three technologies above, based on pointwiseanalysis of a section of road, is that detection of an incident is notimmediate. Indeed, an incident occurring near an analysis point is notdetected; rather, only the later consequences of this incident at themeasurement point are detected. The detection timescale may be verylong, of the order of several minutes.

As we stated earlier, a second presently known analysis techniqueconsists in undertaking an overall analysis of a section of road ormotorway, by monitoring this section over a zone of several hundredmeters, and to do so in such a way as to detect the incidents almostinstantaneously. A single technology, still at the development stage,implements this technique by using a video camera associated with imageprocessing specific to AID. The drawbacks are a detection range, hence amonitoring zone, limited to a few hundred meters, and which may begreatly reduced in the event of poor weather (rain, snow, fog) orlighting conditions, that is to say when the risks of an incident aregreatest.

SUMMARY OF THE INVENTION

The idea of the present invention consists in associating the advantagesafforded by a technique of overall analysis (swiftness of incidentdetection) with the advantages gained through the use of a radar(especially, round-the-clock operation, night and day, for the sameperformance).

More precisely, the subject of the present invention is a process formonitoring the traffic of vehicles able to travel on at least two lanes,of the type consisting in transmitting a UHF wave of predetermined formin accordance with a predefined radiation pattern, in receiving signalsreflected by the vehicles over a given duration of acquisition T_(A),and in processing the signals received in such a way as to detect thevehicles and in calculating, for each distance, the number of vehicles idetected as well as the instantaneous speed V_(i) (t) at a given instantt of each vehicle, the process being characterized in that, theradiation pattern having an axis of aim substantially parallel to thedirection of the lanes, and the steps of acquiring and processing thesignals being iterated over consecutive time intervals (T), itfurthermore includes an automatic incident detection phase consisting inperforming the following iterative steps:

A first step of calculating parameters consisting in:

investigating, at each instant t, whether a vehicle i detected in thecurrent time interval corresponds to a vehicle j detected at an instantt-1! of the preceding time interval;

for all the vehicles i corresponding to a vehicle j, calculating, at theinstant t, their acceleration γ_(i) (t) by applying the relation##EQU1## in which αt represents the duration between the instants t-1!and t,

as well as a prediction V_(i) ^(P) (t+1) of their speed at a time t+1!of the following time interval, by applying the relation

    V.sub.i.sup.P (t+1)=γ.sub.i (t)×αt+V.sub.i (t)

for the other vehicles i, initializing their acceleration γ_(i) (t) tothe zero value;

eliminating from the processing all the other vehicles j notcorresponding to any vehicle i.

A second step of incident detection based on the parameters calculatedin the first step, by detecting the passing of the speed of a vehicle ibelow a first predetermined speed threshold V_(thresh).

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood with reference to thedescription below, given with regard to the appended figures:

FIGS. 1a and 1b illustrate an example of a radiation pattern,respectively in elevation and in plan, of a radar system implementingthe process according to the invention;

FIG. 2 illustrates the main steps of the process according to theinvention;

FIG. 3 details step 6 of calculating parameters of FIG. 2;

FIG. 4 illustrates possible steps of calculating additional parametersaccording to the invention;

FIG. 5 illustrates the automatic detection processing on the basis ofthe parameters calculated in step 6 of FIG. 2;

FIG. 6 represents a schematic of a possible radar system for theimplementation of the process according to the invention.

DISCRIPTION OF THE PREFERRED EMBODIMENTS

The solution proposed for solving the problem of automatic incidentdetection consists in providing a radar system which can use the Dopplereffect in such a way as to be able to extract the instantaneous speed ofthe vehicles detected, and which also has a distance-discriminationcapacity. In what follows we shall describe an example of a possibleradar system for the implementation of the process according to theinvention.

The radiation pattern used on transmission and on reception must be suchthat its axis of aim is substantially parallel to the lanes which onedesires to observe.

Preferably, the radar system is positioned at a height h chosenaccording to the zone to he observed, typically around 10 meters if onewishes to observe a zone over several hundred meters, and to do so insuch a way as to limit the risks of masking one vehicle by another.Moreover, the radar system is preferably placed in the middle of thetraffic lanes, the traffic in these lanes not necessarily flowing in thesame direction. By appropriately choosing the radiation of the radarsystem, the latter will be capable of observing all the traffic lanes aswell as both hard shoulders, in both directions of flow, over a lengthdepending on the range of the radar. The radiation may advantageously besymmetric cosecant squared in azimuth and cosecant squared in elevation,so that all the signals received by the radar are substantially at thesame power, and this irrespective of the position of the vehicledetected in the zone of observation of the radar.

FIGS. 1a and 1b illustrate, by way of nonlimiting example, this type ofradiation respectively in elevation (curve A) and in azimuth (curve B),based on a radar system 1 located at a height h from the road 2. Theradiating antenna is represented here diagrammatically by the reference10. In FIG. 2b it may be noted that the observed zone is a portion ofmotorway consisting of two oppositely flowing lanes 20 separated by acentral reservation 21, and of two hard shoulders 22.

The main steps of the monitoring process according to the invention arerepresented diagrammatically in FIG. 2, for a given distance bracket.

Following the transmission at 3 of a UHF wave of predetermined form andaccording to the radiation pattern described earlier, the signalsreflected by the vehicles present in the observed zone are received at4, over a predetermined duration of acquisition T_(A), of the order of afew hundred milliseconds. The radar used is preferably a pulsed radar,so that the wave transmitted consists of a pulse train, with a carrierfrequency of between 3 and 100 GHz. The use of such a radar allowsdistance discrimination to be obtained directly in respect of thedetected vehicles. It is nevertheless possible to use a linearlyfrequency-modulated continuous-wave radar whose processing chainincludes suitable Fourier transform processing for retrieving thedistance information.

The next step referenced 5, is a conventional step of radar signalprocessing consisting in detecting, for each distance or distancebracket k, vehicles i which are present and in extracting the number ofvehicles i detected as well as their instantaneous speed V_(i) (t) . Inthe case in which the radar used is a pulsed Doppler radar, step 5 canbe carried out conventionally as follows, for each distance bracket k:

A sampling of signals received over the duration of acquisition isundertaken over a number N of values of the signals depending on thedesired speed accuracy.

A fast Fourier transform is undertaken on the N samples obtained so asto obtain a line spectrum.

The local maxima greater than a specified threshold dependent on therange of the radar are sought, the corresponding lines relating to thevehicles detected, and the index of the lines giving the Dopplerfrequency directly, and hence the instantaneous speed of the vehicle.

The above processing can be refined by applying, before undertaking thefast Fourier transform, a weighting window to the samples of signalsobtained, so as to reduce the amplitude of the side lobes of thespectral analysis lines. The weighting window is preferably of Hanningor Hamming type. Moreover, if it is desired to be able to determine thedirection of flow of the vehicles detected, the Fourier transform overthe N samples must be complex, that is to say it must use samplesoriginating from phase and quadrature channels of the radar. It is thennecessary to add a step of calculating the modulus squared of thesignals emanating from the fast Fourier transform before searching forthe local maxima.

The above steps 3, 4 and 5 employ techniques which are well known toradar experts and do not require more detailed description in order tounderstand the invention.

The process according to the invention performs the above three stepsiteratively, over predetermined time intervals T. The number of vehiclesdetected and the instantaneous speed of these vehicles are available foreach distance bracket k at the termination of each time interval T.

The automatic incident detection processing according to the inventionstarts from the principle that an incident is defined as a vehicle whichcomes to a stop. In order to detect an incident at a distance k, theprocess according to the invention proposes to detect the passing of avehicle below a speed threshold. To do this, it is necessary to monitorthe vehicles in each distance bracket over several time intervals T andto determine the change in their speed from one time interval toanother. In FIG. 2 it has been regarded, by way of example, that step 5yields, over the current time interval, a number N₁ of vehicles ilocated at the distance k and possessing an instantaneous speed V_(i)(t). Moreover, the preceding iteration, done over a preceding timeinterval, has furnished a number N₂ of vehicles j also located at thedistance k, and possessing an instantaneous speed V_(j) (t).

From these data, the process according to the invention performs a firststep 6 of calculating certain parameters such as the acceleration γ_(i)(t) of each vehicle i and a prediction V_(i) ^(P) (t+1) of the speedwhich this vehicle will have at a following instant. Since this firststep is performed at each time interval, the accelerations γ_(j) (t-1)and the speed predictions V_(j) ^(P) (t) relating to the vehicles jprocessed over the preceding time interval are also available at theinstant t. At the termination of the first step 6 of calculating theparameters, the process according to the invention undertakes a secondstep 7 of incident detection.

The first step 6 of the incident detection phase according to theinvention will now be described with reference to FIG. 3, whichillustrates the various calculations and tests performed during thisstep 6: the principle of this step consists in investigating whethercertain vehicles i detected at the instant t are among the vehicles jdetected at the instant t-1, this amounting to effecting a kind ofcorrelation for one and the same distance k between some of theparameters calculated at the instant t-1, namely the number N₂ ofvehicles j, their instantaneous speed V_(j) (t-1), and the prediction oftheir speed V_(j) ^(P) (t), and the parameters available at the instantt, namely the number N₁, of vehicles i, and their instantaneous speedV_(i) (t).

As indicated in FIG. 3 by a test 60, the investigation of correspondencebetween a vehicle i and a vehicle j consists in calculating, for eachvehicle i and for each vehicle j, the discrepancy between the predictionV_(j) ^(P) (t) of the speed of the vehicle j and the instantaneous speedV_(i) (t) of the vehicle i, and in then comparing the absolute value ofthe said discrepancy with a predetermined threshold value V_(MAX). Threecases are possible at the termination of this test 60:

If the absolute value of the discrepancy is much less than the thresholdvalue, vehicle i and vehicle j may be regarded as corresponding to oneand the same vehicle. The acceleration γ_(i) (t) of this vehicle is thencalculated at 61 by applying the relation ##EQU2## in which αtrepresents the duration between the two instants t and t-1.

The prediction of the speed of this vehicle at the instant t+1 is thencalculated at 62 by applying the relation

    V.sub.i.sup.P (t+1)=γ.sub.i (t)×Δt+V.sub.i (t)

if a vehicle i detected at the instant t cannot be associated with anyvehicle j (test 60 negative), it must be regarded as being a new vehiclehaving entered the distance bracket k. In this case, the value of itsacceleration γ_(i) (t) is initialized to the zero value at 63, and itsinstantaneous speed V_(i) (t) is made to correspond with the predictionthereof V_(i) ^(P) (t+1).

If a vehicle j detected at the instant t-1 cannot be associated with anyvehicle i (test 60 also negative), this vehicle must be regarded ashaving left the distance bracket k. All the parameters relating to thisvehicle j can then be set to the zero value at 65 so that they may beignored in the remainder of the processing.

At the termination of the first step 6 of calculating parameters, thereare therefore available, for each vehicle i detected, its instantaneousspeed, its acceleration, the prediction of its future speed and, incertain cases, its correspondence with a vehicle j detected during thepreceding time interval, together with the parameters associated withit.

As we stated earlier, the detection of an incident at a distance kconsists in detecting the passing of the speed of a vehicle i below afirst predetermined speed threshold V_(thresh). To do this, the secondstep 7 of the detection phase of the process according to the invention(see FIG. 2) can perform two possible kinds of comparison:

In a first particular case, an incident is regarded as being caused by avehicle i at the distance k when the following two conditions are met:##EQU3##

Another possible variant consists in regarding an incident to be causedby a vehicle i at the distance k when the following two conditions aremet: ##EQU4##

Other facilities may advantageously be added to the process according tothe invention: as shown in FIG. 4, it may in particular be beneficial toprovide a step 8 of calculating the mean speed of the vehicles detectedover several time intervals, or more generally over a predeterminedduration D, and to do so for each distance bracket k, as well as acalculation step 9 making it possible to extract the maximum and minimumspeeds calculated over this duration D for a given distance bracket k.In FIG. 4, the duration D has been assumed to correspond to m successivetime intervals, a time interval j, for j varying from 1 to m, making itpossible to detect a number N_(j) of vehicles for a given distance k,and to deliver, at an instant t_(j), the instantaneous speed V_(jk)(t_(j)) of each vehicle.

FIG. 4 also shows a step 8a of calculating the standard deviation of themean speed V_(mean) (k) for a given distance bracket k.

The mean speed per distance bracket information can advantageously beused, according to the process of the invention, to perform automaticholdup or slowdown detection processing.

Such processing is represented in simplified schematic form in FIG. 5.

It consists in analysing the mean speeds supplied in step 8 (FIG. 4) fortwo successive distance brackets k and k+1 by comparing them at 11 witha second speed threshold V_(mthresh) defined beforehand.

The interpretation of the result of the comparison made at 11 depends onthe direction of flow of the vehicles:

For vehicles receding from the radar system, a start of slowdown isdetected at 11a when

    V.sub.mean (k)>V.sub.mthresh and V.sub.mean (k+1)<V.sub.mthresh

and, an end of sslowdown is detected at 11b when

    V.sub.mean (k)<V.sub.mthresh and V.sub.mean (k+1)>V.sub.mthresh

The above conclusions must be reversed when considering vehiclesapproaching the radar.

The automatic holdup or slowdown detection processing can advantageouslybe used to disable the incident detection phase. Indeed, in the case ofa holdup, there is a risk of the number of incidents detected byapplying the process according to the invention being large, and it maybe useful to undertake a filtering of the incidents so as to limit thevolume thereof.

Still with the aim of reducing the volume of processing during theautomatic incident detection phase, the analysis can be restricted tothe vehicles possessing a moderate speed, for example 40 km/h, thesehaving a greater probability of generating an incident.

The process according to the invention can furthermore be improved bystatistical processing represented diagrammatically at 7' in FIG. 2.

Firstly, the notion of a potential incident can be extended, accordingto a variant of the process according to the invention, to any vehicletravelling at an abnormally low speed relative to the traffic flow inthe lanes.

In order to indicate the presence of a slow vehicle in a given distancebracket, it is sought to detect any vehicle travelling at a speed belowa predefined threshold speed V_(inf), and this for a duration greaterthan a limit duration D_(lim).

The phase of automatic incident detection applied to this particularvehicle is then supplemented, according to the invention, with a stepduring which a check is made, at the end of the limit duration D_(lim),as to whether the vehicle with instantaneous speed less than V_(inf) isstill detected in the relevant speed bracket. In this case, an alarm isgenerated indicating the presence of a slow vehicle.

Moreover, it may be beneficial, starting from the automatic incidentdetection process which is the subject of the invention, to extract, fora predetermined reference distance bracket k_(ref) and per direction offlow, certain parameters representative of the state of the traffic inthe monitored zone, such as the accurate counting of vehiclestravelling, during a particular analysis period T_(ref), in the distancebracket k_(ref), the mean speed of these vehicles, the occupancy factorfor the carriageways per direction of flow, or again the heavy goodsvehicle factor. We shall detail below the steps of the process accordingto the invention making it possible to obtain the above parameters,these steps being shown diagrammatically at 7' in FIG. 2.

As regards the counting of the vehicles in the distance bracket k_(ref),over the duration T_(ref), the first step of the process according tothe invention makes it possible, as we saw earlier, to detect, at aninstant t, the appearance of new vehicles, that is to say those forwhich the acceleration was initialized to the value zero (step 63, FIG.3), and to detect the disappearance of vehicles, that is to say thosewhich have been eliminated from the processing (step 65, FIG. 3).

Thus, based on a plurality of automatic detection phases 6, 7, it ispossible to count the number N_(ref) of vehicles having passed throughthe distance bracket k_(ref) during the analysis period T_(ref) by usingthe results obtained in the first step of the phases. Preferably, eachvehicle is counted from the moment at which the first parametercalculation step 6 eliminates it from the processing.

The mean speed V over the distance bracket k_(ref) can then be deducedby applying the relation: ##EQU5## in which V_(i) is the instantaneousspeed of each vehicle counted.

The occupancy factor for the distance bracket k_(ref) is defined by thepresence factor for a vehicle in the distance bracket k_(ref) during theanalysis period T_(ref). In a variant of the process according to theinvention, an additional calculation step makes it possible to supplythis occupancy factor T_(occ) by applying the relation: ##EQU6## inwhich: N_(i) is the number of detections per direction of flow obtainedaccording to the process of the invention during the analysis periodT_(ref),

M is the number of automatic incident detection phases undertaken duringT_(ref),

Q is the number of lanes in the direction of flow.

Finally, the time of presence T_(p) of each vehicle in the distancebracket K_(ref), and thence the length L of these vehicles, can bededuced from the previously described vehicle counting step by applyingthe relation:

    L=VT.sub.p

where V is the instantaneous speed of these vehicles. This may make itpossible to classify the vehicles according to their length,distinguishing between heavy goods vehicles, which have a length greaterthan a predefined length L_(thresh), and lightweight vehicles.

The extraction of these parameters can be used to calculate inparticular the heavy goods vehicles factor for the distance bracketk_(ref) during the analysis period.

FIG. 6 illustrates an example of a radar system for implementing theprocess according to the invention, in simplified schematic form:

Here the radar used is a pulsed radar with carrier frequency chosenbetween 3 and 100 GHz. The higher the frequency, the more compact willbe the antenna 10 used for transmission and reception. A pulse generator11' shapes the pulses which are amplified by the transmitter 12.Synchronization 13 allows the transmitter and receiver 14 to operatealternately, the latter receiving and amplifying the signals received bythe antenna 10 via a circulator 15. The analog signals from the receiverare conventionally digitized in a coding module 16, the samples beingprocessed in a signal processing module 17 so as to detect the vehicles,their speeds and their distances from the radar. An informationprocessing module 18 makes it possible to implement the automaticincident and sslowdown detection phases of the process according to theinvention. An interface 19 then makes it possible to transmit theinformation (slowdown or incident) to an information management centre.

By way of nonlimiting example, such a radar system can have a distanceresolution of around 10 meters, compatible with the dimensions of thevehicles, an unambiguous speed range of ±200 km/h, and a speedresolution of around 3 km/h.

The radar system described above can be used anywhere in which rapidknowledge is desired of incidence occurring thereat or of the state ofthe traffic. The information supplied by the module 18 can be utilizedautomatically by traffic management systems, especially with the aim ofinforming the users of the road directly, by way of variable-messageroad signs.

I claim:
 1. Process for monitoring the traffic of vehicles able totravel on at least two lanes, of the type consisting in transmitting aUFH wave of predetermined form in accordance with a predefined radiationpattern, in receiving signals reflected by the vehicles over a givenduration of acquisition T_(A), and in processing the signals received insuch a way as to detect the vehicles and in calculating, for eachdistance, the number of vehicles i detected as well as the instantaneousspeed V_(i) (t) at a given instant t of each vehicle, the processwherein, the radiation pattern having an axis of aim substantiallyparallel to the direction of the lanes, and the steps of acquiring andprocessing the signals being iterated over consecutive time intervals,it furthermore includes an automatic incident detection phase comprisingperforming the following iterative steps:A first step of calculatingparameters including:investigating, at each instant t, whether a vehiclei detected in the current time interval corresponds to a vehicle jdetected at an instant of the preceding time interval; for all thevehicles i corresponding to a vehicle j, calculating at the instant t,their acceleration γ_(i) (t) by applying the relation ##EQU7## in whichαt represents the duration between the instants and t, as well as aprediction V_(i) ^(P) (t+1) of their speed at a time of the followingtime interval, by applying the relation

    V.sub.i.sup.P (t+1)=γ.sub.i (t)×Δt+V.sub.i (t)

for the other vehicles i, initializing their acceleration γ_(i) (t) tothe zero value; eliminating from the processing all the other vehicles jnot corresponding to any vehicle; and A second step of incidentdetection based on the parameters calculated in the first step, bydetecting the passing of the speed of a vehicle i below a firstpredetermined speed threshold V_(thresh).
 2. Process according to claim1, wherein the investigating of correspondence between a vehicle i and avehicle j comprises calculating, for each vehicle i and for each vehiclej, the discrepancy between the prediction V_(j) ^(P) (t) of the speed ofvehicle j and the instantaneous speed V_(i) (t) of vehicle i, and inthen comparing the absolute value of the discrepancy with apredetermined threshold value V_(max) beyond which vehicles i and j areregarded as distinct.
 3. Process according to claim 2, wherein thesecond step of incident detection comprises comparing, for each vehiclei, the speed V_(i) (t) with the first speed threshold V_(thresh), andthe acceleration γ_(i) (t) with the zero value, and in detecting anincident when the speed V_(i) (t) and the acceleration are respectivelyless than the first speed threshold V_(thresh) and the zero value. 4.Process according to claim 2, wherein the second step of incidentdetection comprises comparing, for each vehicle i, the speed V_(i) (t)and the prediction V_(i) ^(P) (t+1) of the speed with respect to thesaid given first speed threshold V_(thresh), and in detecting anincident when the speed V_(i) (t) and the prediction V_(i) ^(P) (t+1)are respectively greater than and less than the first speed thresholdV_(thresh).
 5. Process according to claim 2, of calculating the meansspeed of all the vehicles detected, at a given distance k, over apredetermined duration D.
 6. Process according to claim 1, wherein thesecond step of incident detection comprises comparing, for each vehiclei, the speed V_(i) (t) with the first speed threshold V_(thresh), andthe acceleration γ_(i) (t) with the zero value, and in detecting anincident when the speed V_(i) (t) and the acceleration are respectivelyless than the first speed threshold V_(thresh) and the zero value. 7.Process according to claim 6, of calculating the means speed of all thevehicles detected, at a given distance k, over a predetermined durationD.
 8. Process according to claim 1, wherein the second step of incidentdetection comprises comparing, for each vehicle i, the speed V_(i) (t)and the prediction V_(i) (t+1) of the speed with respect to the saidgiven first speed threshold V_(thresh), and in detecting an incidentwhen the speed V_(i) (t) and the prediction V_(i) ^(P) (t+1) arerespectively greater than and less than the first speed thresholdV_(thresh).
 9. Process according to claim 8, further comprising a stepof calculating the means speed of all the vehicles detected, at a givendistance k, over a predetermined duration D.
 10. Process according toclaim 1, further comprising a step of calculating the mean speed of allthe vehicles detected, at a given distance k, over a predeterminedduration D.
 11. Processing according to claim 10, further comprising astep of extracting the maximum and minimum speeds calculated in regardto the vehicles detected at a given distance over the duration D. 12.Process according to claim 11, further comprising a step of calculatingthe standard deviation of the speed over the duration D.
 13. Processaccording to claim 10, further comprising a step of calculating thestandard deviation of the speed over the duration D.
 14. Processingaccording to claim 10, further comprising a holdup or slowdown detectionstep including:effecting a comparison of the mean speeds V_(mean) (k)and V_(mean) (k+1) calculated for two successive distances k, k+1, withrespect to a second speed threshold V_(mthresh), detecting a start ofslowdown or an end of slowdown on the basis of the result of thecomparison.
 15. Processing according to claim 14, wherein the holdupdetection step disables the automatic incident detection phase when aslowdown has been detected.
 16. Process according to claim 1, whereinthe automatic incident detection phase is applied to any vehicle whoseinstantaneous speed is less than a predefined threshold speed V_(inf),and in that it furthermore includes a step in which a check is made asto whether a vehicle is still detected after a duration greater than alimit duration D_(lim).
 17. Process according to claim 1, furthercomprising several successive phases of automatic detection ofincidents, and in that the first step of the said phases is furthermoreused to undertake a counting of the vehicles passing through a referencedistance bracket k_(ref) during a predefined analysis period T_(ref).18. Process according to claim 17, further comprising a step ofcalculating the mean speed V of the vehicles over the distance bracketk_(ref) during the analysis period T_(ref).
 19. Process according toclaim 17, further comprising a step of calculating the occupancy factorT_(occ) of the distance bracket k_(ref) during the analysis periodT_(ref) by applying the relation: ##EQU8## in which: N₁ is the number ofdetections per direction of flow obtained according to the process ofthe invention during the analysis period T_(ref),M is the number ofautomatic incident detection phases undertaken during T_(ref), Q is thenumber of lanes in the direction of flow.
 20. Process according to claim17, further comprising a step of calculating the time of presence T_(p)of a vehicle in the distance bracket k_(ref), and its length.